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        <p><a href="https://blog.csdn.net/Hanani_Jia/article/details/77950594" target="_blank" rel="noopener">GItHub使用指南</a><br><a href="https://blog.csdn.net/ljc_563812704/article/details/53464039" target="_blank" rel="noopener">GitHub 编辑指导</a><br><a href="https://www.jianshu.com/p/fd97e1f8f699" target="_blank" rel="noopener">GItHub 公式编辑</a><br><a href="https://hacpai.com/guide/markdown" target="_blank" rel="noopener">Markdown 教程</a></p>
<h2 id="程序员四大件"><a href="#程序员四大件" class="headerlink" title="程序员四大件"></a>程序员四大件</h2><ul>
<li>数据结构与算法</li>
<li>操作系统</li>
<li>计算机网络</li>
<li>设计模式</li>
</ul>
<h2 id="操作系统"><a href="#操作系统" class="headerlink" title="操作系统"></a>操作系统</h2><h3 id="进程和线程的区别"><a href="#进程和线程的区别" class="headerlink" title="进程和线程的区别"></a><a href="https://blog.csdn.net/ThinkWon/article/details/102021274#t3" target="_blank" rel="noopener">进程和线程的区别</a></h3><ol>
<li><strong>根本区别：</strong> 进程是资源调度的基本单位，线程是CPU调度和执行的基本单位。</li>
<li><strong>资源开销：</strong> 每个进程都有独立的代码和数据空间（程序上下文），程序之间的切换会有较大的开销；线程可以看做轻量级的进程，同一类线程共享代码和数据空间，每个线程都有自己独立的运行栈（保证线程中的局部变量不被别的线程访问到）和程序计数器（为了线程切换后能恢复到正确的执行位置），线程之间切换的开销小。</li>
<li><strong>包含关系：</strong> 如果一个进程内有多个线程，则执行过程不是一条线的，而是多条线（线程）共同完成的；线程是进程的一部分，所以线程也被称为轻权进程或者轻量级进程。</li>
<li><strong>内存分配：</strong> 同一进程的线程共享本进程的地址空间和资源，而进程之间的地址空间和资源是相互独立的。</li>
<li><strong>影响关系：</strong> 一个进程崩溃后，在保护模式下不会对其他进程产生影响，但是一个线程崩溃整个进程都死掉。所以多进程比多线程健壮。</li>
<li><strong>执行过程：</strong> 每个独立的进程有程序运行的入口、顺序指向序列和程序出口。但是线程不能独立执行，必须依存在应用程序中，有应用程序提供多个线程指向控制，两者均可并发执行。</li>
</ol>
<p><a href="https://blog.csdn.net/linraise/article/details/12979473" target="_blank" rel="noopener">多进程和多线程的区别</a></p>
<div class="table-container">
<table>
<thead>
<tr>
<th>纬度　</th>
<th>多进程</th>
<th>多线程　</th>
<th>　总结　</th>
</tr>
</thead>
<tbody>
<tr>
<td>数据共享、同步　</td>
<td>数据是分开的：共享复杂，需要用IPC；同步简单</td>
<td>多线程共享进程数据：共享简单；同步复杂</td>
<td>各有优势</td>
</tr>
<tr>
<td>内存、CPU</td>
<td>占用内存多，切换复杂，CPU利用率第</td>
<td>占用内存少，切换简单，CPU利用率高</td>
<td>线程占优</td>
</tr>
<tr>
<td>创建销毁、切换</td>
<td>创建销毁、切换复杂，速度慢</td>
<td>创建销毁、切换简单，速度快</td>
<td>线程占优</td>
</tr>
<tr>
<td>编程调试</td>
<td>编程简单，调试简单</td>
<td>编程复杂，调试复杂</td>
<td>进程占优</td>
</tr>
<tr>
<td>可靠性</td>
<td>进程之间不会相互影响</td>
<td>一个线程挂掉将导致整个进程挂掉</td>
<td>进程占优</td>
</tr>
<tr>
<td>分布式</td>
<td>适应于多核、多机分布；如果一台机器不够，扩展到多台机器比较简单</td>
<td>适应于多核分布</td>
<td>进程占优</td>
</tr>
</tbody>
</table>
</div>
<h3 id="并发与并行"><a href="#并发与并行" class="headerlink" title="并发与并行"></a><a href="https://blog.csdn.net/qq_33290787/article/details/51790605" target="_blank" rel="noopener">并发与并行</a></h3><ol>
<li>并发的实质是一个物理CPU（也可以多个物理CPU）在若干道程序（或线程）之间多路复用，并发性是对有限物理资源强行限制使多用户共享以提高效率。</li>
<li>并行指两个或两个以上的事件（或线程）在同一时刻发生，是真正意义上的不同事件或线程在同一时刻，在不同CPU资源上（多核）同时执行。</li>
</ol>
<h2 id="数据结构和算法问题"><a href="#数据结构和算法问题" class="headerlink" title="数据结构和算法问题"></a>数据结构和算法问题</h2><h3 id="堆和栈的区别"><a href="#堆和栈的区别" class="headerlink" title="堆和栈的区别"></a><a href="https://blog.csdn.net/hairetz/article/details/4141043" target="_blank" rel="noopener">堆和栈的区别</a></h3><h2 id="计算机网络"><a href="#计算机网络" class="headerlink" title="计算机网络"></a>计算机网络</h2><p><img src="/2019/01/23/面试问题收集/网络协议.png" alt="网络协议"></p>
<p><a href="https://zhuanlan.zhihu.com/p/147370653" target="_blank" rel="noopener">TCP/IP协议</a></p>
<h2 id="计算机视觉面试问题"><a href="#计算机视觉面试问题" class="headerlink" title="计算机视觉面试问题"></a>计算机视觉面试问题</h2><h3 id="SVM"><a href="#SVM" class="headerlink" title="SVM"></a><a href="https://blog.csdn.net/v_july_v/article/details/7624837" target="_blank" rel="noopener">SVM</a></h3><h3 id="CNN"><a href="#CNN" class="headerlink" title="CNN"></a><a href="https://blog.csdn.net/fengbingchun/article/details/50529500" target="_blank" rel="noopener">CNN</a></h3><p><a href="https://blog.csdn.net/weixin_42111770/article/details/80719302" target="_blank" rel="noopener">常见网络收集</a>  </p>
<h3 id="softmax函数"><a href="#softmax函数" class="headerlink" title="softmax函数"></a><a href="https://blog.csdn.net/u014380165/article/details/77284921" target="_blank" rel="noopener">softmax函数</a></h3><h3 id="attention"><a href="#attention" class="headerlink" title="attention"></a><a href="https://blog.csdn.net/guohao_zhang/article/details/79540014" target="_blank" rel="noopener">attention</a></h3><h3 id="data-augmentation"><a href="#data-augmentation" class="headerlink" title="data augmentation"></a>data augmentation</h3><h3 id="损失函数"><a href="#损失函数" class="headerlink" title="损失函数"></a>损失函数</h3><h4 id="随机梯度下降"><a href="#随机梯度下降" class="headerlink" title="随机梯度下降"></a>随机梯度下降</h4><h4 id="交叉熵"><a href="#交叉熵" class="headerlink" title="交叉熵"></a><a href="https://blog.csdn.net/rtygbwwwerr/article/details/50778098" target="_blank" rel="noopener">交叉熵</a></h4><h3 id="正则化"><a href="#正则化" class="headerlink" title="正则化"></a><a href="https://blog.csdn.net/kyang624823/article/details/78646234" target="_blank" rel="noopener">正则化</a></h3><h3 id="泰勒公式"><a href="#泰勒公式" class="headerlink" title="泰勒公式"></a><a href="https://charlesliuyx.github.io/2018/02/16/%E3%80%90%E7%9B%B4%E8%A7%82%E8%AF%A6%E8%A7%A3%E3%80%91%E6%B3%B0%E5%8B%92%E7%BA%A7%E6%95%B0/" target="_blank" rel="noopener">泰勒公式</a></h3><h3 id="Batch-Normalization"><a href="#Batch-Normalization" class="headerlink" title="Batch Normalization"></a><a href="https://blog.csdn.net/qq_25737169/article/details/79048516" target="_blank" rel="noopener">Batch Normalization</a></h3><h3 id="网络参数是如何计算的"><a href="#网络参数是如何计算的" class="headerlink" title="网络参数是如何计算的"></a>网络参数是如何计算的</h3><h3 id="ShuffleNet"><a href="#ShuffleNet" class="headerlink" title="ShuffleNet"></a><a href="https://blog.csdn.net/u011974639/article/details/79200559" target="_blank" rel="noopener">ShuffleNet</a></h3><h3 id="deepwise-separable-conv"><a href="#deepwise-separable-conv" class="headerlink" title="deepwise separable conv"></a><a href="https://yinguobing.com/separable-convolution/#fn2" target="_blank" rel="noopener">deepwise separable conv</a></h3><h3 id="最优化方法"><a href="#最优化方法" class="headerlink" title="最优化方法"></a><a href="http://www.cnblogs.com/maybe2030/p/4751804.html#_label0" target="_blank" rel="noopener">最优化方法</a></h3><h3 id="深度神经网络全面概述：从基本概念到实际模型和硬件基础"><a href="#深度神经网络全面概述：从基本概念到实际模型和硬件基础" class="headerlink" title="深度神经网络全面概述：从基本概念到实际模型和硬件基础"></a><a href="https://cloud.tencent.com/developer/article/1116764" target="_blank" rel="noopener">深度神经网络全面概述：从基本概念到实际模型和硬件基础</a></h3><h3 id="数学概念"><a href="#数学概念" class="headerlink" title="数学概念"></a><a href="https://blog.csdn.net/majinlei121/article/details/47260917" target="_blank" rel="noopener">数学概念</a></h3><h3 id="RNN"><a href="#RNN" class="headerlink" title="RNN"></a><a href="https://blog.csdn.net/heyongluoyao8/article/details/48636251" target="_blank" rel="noopener">RNN</a></h3><h3 id="LSTM"><a href="#LSTM" class="headerlink" title="LSTM"></a><a href="https://blog.csdn.net/gzj_1101/article/details/79376798" target="_blank" rel="noopener">LSTM</a></h3><h3 id="RPN"><a href="#RPN" class="headerlink" title="RPN"></a><a href="https://blog.csdn.net/sloanqin/article/details/51545125" target="_blank" rel="noopener">RPN</a></h3><h3 id="PCA"><a href="#PCA" class="headerlink" title="PCA"></a>PCA</h3><h3 id="K-means"><a href="#K-means" class="headerlink" title="K-means"></a>K-means</h3><p>基本流程：</p>
<ol>
<li>初始化k个聚类中心$c_1$, $c_2$,…,$c_k$</li>
<li>对于每个样本$x_i$和每个聚类中心$c_j$，计算样本和聚类中心之间的距离$d_ij$</li>
<li>对于每个样本$x_i$，基于最小的$d_ij$把其分配到第$j$个类$C_j$</li>
<li>对于每个类$C_j$，计算其所有样本的均值作为新的聚类中心，重复步骤2和步骤3直至样本点所属的类不再变化或达到最大迭代次数 </li>
</ol>
<h3 id="KNN"><a href="#KNN" class="headerlink" title="KNN"></a>KNN</h3><p>基本流程：</p>
<ol>
<li>计算测试数据与各个训练数据之间的距离</li>
<li>按照距离的递增关系进行排序</li>
<li>选取距离最小的K个点</li>
<li>确定前K个点所在类别的出现概率</li>
<li>返回前K个点中出现频率最高的类别作为测试数据的预测分类</li>
</ol>
<h3 id="K-means和KNN的区别"><a href="#K-means和KNN的区别" class="headerlink" title="K-means和KNN的区别"></a>K-means和KNN的区别</h3><ul>
<li>K-means是无监督学习算法，KNN是有监督学习算法</li>
<li>K-means是聚类算法，KNN是分类算法</li>
<li>K-means有明显的训练过程（求聚类中心），KNN在学习阶段只是简单的吧所有样本记录</li>
<li>在测试阶段，对于K-means，新的样本点的判别与聚类中心有关，即与所有训练样本有关，对于KNN，新的样本点的判别只是与最近相邻的K个样本有关</li>
</ul>
<h3 id="损失函数-1"><a href="#损失函数-1" class="headerlink" title="损失函数"></a><a href="https://blog.csdn.net/kangyi411/article/details/78969642" target="_blank" rel="noopener">损失函数</a></h3><h3 id="梯度消失和梯度爆炸"><a href="#梯度消失和梯度爆炸" class="headerlink" title="梯度消失和梯度爆炸"></a><a href="https://blog.csdn.net/qq_25737169/article/details/78847691" target="_blank" rel="noopener">梯度消失和梯度爆炸</a></h3><p>解决方法：</p>
<ol>
<li>预训练加微调</li>
<li>梯度剪切、正则</li>
<li>使用relu、leakrelu、elu等激活函数</li>
<li>batchnorm</li>
<li>残差结构</li>
<li>LSTM</li>
</ol>
<h3 id="SITF"><a href="#SITF" class="headerlink" title="SITF"></a>SITF</h3><h3 id="Dropout"><a href="#Dropout" class="headerlink" title="Dropout"></a>Dropout</h3><h3 id="Pooling"><a href="#Pooling" class="headerlink" title="Pooling"></a>Pooling</h3><h3 id="正则化-1"><a href="#正则化-1" class="headerlink" title="正则化"></a><a href="https://charlesliuyx.github.io/2017/10/03/%E3%80%90%E7%9B%B4%E8%A7%82%E8%AF%A6%E8%A7%A3%E3%80%91%E4%BB%80%E4%B9%88%E6%98%AF%E6%AD%A3%E5%88%99%E5%8C%96/#Why-amp-What-%E6%AD%A3%E5%88%99%E5%8C%96" target="_blank" rel="noopener">正则化</a></h3><h2 id="计算机视觉及深度学习岗位应聘问题"><a href="#计算机视觉及深度学习岗位应聘问题" class="headerlink" title="计算机视觉及深度学习岗位应聘问题"></a><a href="https://blog.csdn.net/ferriswym/article/details/81331191" target="_blank" rel="noopener">计算机视觉及深度学习岗位应聘问题</a></h2>
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          <div class="post-toc motion-element"><ol class="nav"><li class="nav-item nav-level-2"><a class="nav-link" href="#程序员四大件"><span class="nav-number">1.</span> <span class="nav-text">程序员四大件</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#操作系统"><span class="nav-number">2.</span> <span class="nav-text">操作系统</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#进程和线程的区别"><span class="nav-number">2.1.</span> <span class="nav-text">进程和线程的区别</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#并发与并行"><span class="nav-number">2.2.</span> <span class="nav-text">并发与并行</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#数据结构和算法问题"><span class="nav-number">3.</span> <span class="nav-text">数据结构和算法问题</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#堆和栈的区别"><span class="nav-number">3.1.</span> <span class="nav-text">堆和栈的区别</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#计算机网络"><span class="nav-number">4.</span> <span class="nav-text">计算机网络</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#计算机视觉面试问题"><span class="nav-number">5.</span> <span class="nav-text">计算机视觉面试问题</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#SVM"><span class="nav-number">5.1.</span> <span class="nav-text">SVM</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#CNN"><span class="nav-number">5.2.</span> <span class="nav-text">CNN</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#softmax函数"><span class="nav-number">5.3.</span> <span class="nav-text">softmax函数</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#attention"><span class="nav-number">5.4.</span> <span class="nav-text">attention</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#data-augmentation"><span class="nav-number">5.5.</span> <span class="nav-text">data augmentation</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#损失函数"><span class="nav-number">5.6.</span> <span class="nav-text">损失函数</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#随机梯度下降"><span class="nav-number">5.6.1.</span> <span class="nav-text">随机梯度下降</span></a></li><li class="nav-item nav-level-4"><a class="nav-link" href="#交叉熵"><span class="nav-number">5.6.2.</span> <span class="nav-text">交叉熵</span></a></li></ol></li><li class="nav-item nav-level-3"><a class="nav-link" href="#正则化"><span class="nav-number">5.7.</span> <span class="nav-text">正则化</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#泰勒公式"><span class="nav-number">5.8.</span> <span class="nav-text">泰勒公式</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#Batch-Normalization"><span class="nav-number">5.9.</span> <span class="nav-text">Batch Normalization</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#网络参数是如何计算的"><span class="nav-number">5.10.</span> <span class="nav-text">网络参数是如何计算的</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#ShuffleNet"><span class="nav-number">5.11.</span> <span class="nav-text">ShuffleNet</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#deepwise-separable-conv"><span class="nav-number">5.12.</span> <span class="nav-text">deepwise separable conv</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#最优化方法"><span class="nav-number">5.13.</span> <span class="nav-text">最优化方法</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#深度神经网络全面概述：从基本概念到实际模型和硬件基础"><span class="nav-number">5.14.</span> <span class="nav-text">深度神经网络全面概述：从基本概念到实际模型和硬件基础</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" 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